3.8천건 · 관련도순
End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.
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End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains.
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End-to-end Ribo-seq analysis from FASTQ through periodicity QC, P-site calibration, ORF detection, translation efficiency, and stalling. Use when orchestrating a full ribosome profiling pipeline and deciding harvest/dedup/alignment options and which downstream analyses the library can support.
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End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.
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End-to-end multiome workflow for joint scRNA-seq + scATAC-seq analysis. Covers data loading, separate modality processing, and WNN integration with Seurat/Signac. Use when analyzing joint scRNA+scATAC data.
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End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.
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Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.
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End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies.
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Orchestrates the full path from differential expression results to redundancy-collapsed functional enrichment: choose ORA vs GSEA, convert gene IDs per method, run enrichGO/enrichKEGG/enrichPathway/enrichWP or gseGO/gseKEGG (clusterProfiler, ReactomePA, rWikiPathways), and visualize. Use when a DESeq2/edgeR/limma result must become enriched GO terms, KEGG/Reactome/WikiPathways pathways, or a GSEA leading edge; when the input is a full ranking for all genes (GSEA, named decreasing vector) or only a pre-selected list (ORA plus a defensible background universe); or when assembling DE-to-pathway end to end. The DE list and ranking statistic come from differential-expression/de-results; per-method nuance lives in the pathway-analysis skills.
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Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.
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End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, leakage-safe cross-validation, calibration, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.
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Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Profiles the genome first (k-mer spectrum -> size, heterozygosity, ploidy), QCs reads, chooses an assembly path by data type (SPAdes for Illumina, Flye for noisy long reads, hifiasm for HiFi, metaFlye for communities), polishes only when needed, decontaminates, scaffolds with Hi-C, and finishes with three-axis QC (contiguity + completeness + correctness). Use when assembling a genome from raw reads and deciding which assembler, whether to polish, and how to prove the result is good.
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Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.
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End-to-end shotgun metagenomics workflow from FASTQ to taxonomic and functional profiles, orchestrating controls/host depletion, Kraken2+Bracken classification, MetaPhlAn marker profiling, and HUMAnN functional profiling. Covers the controls-first ordering, why Kraken2 read counts are not abundances and MetaPhlAn cell fractions do not equal Bracken read fractions, and the consistent-pipeline framing. Use when profiling shotgun metagenomic samples end to end, or chaining classification, abundance, and function. For resistome see metagenomics/amr-detection; for strains see metagenomics/strain-tracking; for assembly see genome-assembly/metagenome-assembly.
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Install or update ToolUniverse in Claude Science — create the conda env, install the tooluniverse pip package, and (re)build the tooluniverse-research skill by fetching the current workflow library from GitHub. Use for first-time setup, upgrading the ToolUniverse version, refreshing the bundled workflows after an upstream release, or reinstalling on a new machine.
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End-to-end Hi-C analysis workflow from FASTQ to compartments, TADs, and loops, with the decision of WHICH features the sequencing depth can support. Covers pairtools read-pair processing and library QC, cooler matrices, ICE balancing and distance-decay expected, A/B compartments, TAD boundaries, loop calling, and the routing of HiChIP/PLAC-seq/Capture Hi-C to protein-directed loop callers. Use when processing Hi-C data end to end, deciding a resolution for a given depth, or choosing between bulk-Hi-C and protein-directed loop calling.
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Authors portable, strongly-typed bioinformatics pipelines in the Common Workflow Language (CWL v1.2) as CommandLineTool/Workflow/ExpressionTool documents, validated with cwltool and run at scale on Toil/Arvados/Calrissian. Use when deciding CWL (portability/provenance/regulated) vs Nextflow/WDL/Snakemake; declaring secondaryFiles for indexed companions (.bai/.fai/.dict/.tbi and the caret rule); putting resources/containers under requirements (must-hold) vs hints (advisory) to avoid silent OOM; choosing scatterMethod (dotproduct vs flat_/nested_crossproduct); preferring $(...) parameter refs over ${...} JavaScript for portability; pinning DockerRequirement images; or emitting a CWLProv provenance object for audited/clinical settings.
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Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.
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End-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition with MANDATORY PERMANOVA + PERMDISP pair (Anderson & Walsh 2013), constrained ordination, and the read-counts-not-abundance critique (Lamb 2019). Use when processing eDNA samples for biodiversity assessment, deciding ASV vs OTU, configuring OBITools3 v3, interpreting decontam screening, or reporting community comparisons with the dispersion confound check.
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End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.
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End-to-end flow, spectral, and mass cytometry (CyTOF) pipeline from raw FCS files to differentially abundant/expressed cell populations. Orchestrates the read -> compensate/unmix -> transform -> QC -> doublet-removal -> cluster-or-gate -> annotate -> diffcyt DA/DS chain with flowCore/CATALYST/diffcyt, branching on instrument type and on clustering-vs-gating. Use when processing a cytometry experiment end-to-end, deciding the pipeline path for an instrument, or wiring the flow-cytometry component skills into one analysis with valid sample-level statistics.
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End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.
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Orchestrates the untargeted LC-MS metabolomics pipeline end-to-end (xcms 4.x feature extraction, QC/drift/normalization, confidence-stratified annotation, permutation-validated statistics, background-aware pathway mapping), naming what each stage decides and where it silently fails. Use when running a full LC-MS metabolomics study from raw mzML to enriched pathways and needing the honest handoffs between stages. Each stage defers to its component skill for parameters and traps; for stable-isotope flux (a separate branch, not this untargeted flow) see metabolomics/isotope-tracing.
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End-to-end bisulfite sequencing workflow from FASTQ to differentially methylated regions. Covers Bismark alignment, methylation calling, and DMR detection with methylKit. Use when analyzing bisulfite sequencing data.
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